%0 Journal Article %1 Prudhomme2019 %A Prudhomme, Claire %A Homburg, Timo %A Ponciano, Jean-Jacques %A Boochs, Frank %A Cruz, Christophe %A Roxin, Ana-Maria %C Cham, Switzerland %D 2019 %I Springer International Publishing %J Computing %K imported %P 1--27 %R 10.1007/s00607-019-00701-y %T Interpretation and automatic integration of geospatial data into the Semantic Web %U https://doi.org/10.1007/s00607-019-00701-y %X In the context of disaster management, geospatial information plays a crucial role in the decision-making process to protect and save the population. Gathering a maximum of information from different sources to oversee the current situation is a complex task due to the diversity of data formats and structures. Although several approaches have been designed to integrate data from different sources into an ontology, they mainly require background knowledge of the data. However, non-standard data set schema (NSDS) of relational geospatial data retrieved from e.g. web feature services are not always documented. This lack of background knowledge is a major challenge for automatic semantic data integration. Focusing on this problem, this article presents an automatic approach for geospatial data integration in NSDS. This approach does a schema mapping according to the result of an ontology matching corresponding to a semantic interpretation process. This process is based on geocoding and natural language processing. This article extends work done in a previous publication by an improved unit detection algorithm, data quality and provenance enrichments, the detection of feature clusters. It also presents an improved evaluation process to better assess the performance of this approach compared to a manually created ontology. These experiments have shown the automatic approach obtains an error of semantic interpretation around 10\% according to a manual approach.

@article{Prudhomme2019, abstract = {In the context of disaster management, geospatial information plays a crucial role in the decision-making process to protect and save the population. Gathering a maximum of information from different sources to oversee the current situation is a complex task due to the diversity of data formats and structures. Although several approaches have been designed to integrate data from different sources into an ontology, they mainly require background knowledge of the data. However, non-standard data set schema (NSDS) of relational geospatial data retrieved from e.g. web feature services are not always documented. This lack of background knowledge is a major challenge for automatic semantic data integration. Focusing on this problem, this article presents an automatic approach for geospatial data integration in NSDS. This approach does a schema mapping according to the result of an ontology matching corresponding to a semantic interpretation process. This process is based on geocoding and natural language processing. This article extends work done in a previous publication by an improved unit detection algorithm, data quality and provenance enrichments, the detection of feature clusters. It also presents an improved evaluation process to better assess the performance of this approach compared to a manually created ontology. These experiments have shown the automatic approach obtains an error of semantic interpretation around 10\% according to a manual approach.}, added-at = {2019-09-17T13:26:44.000+0200}, address = {Cham, Switzerland}, author = {Prudhomme, Claire and Homburg, Timo and Ponciano, Jean-Jacques and Boochs, Frank and Cruz, Christophe and Roxin, Ana-Maria}, biburl = {https://www.bibsonomy.org/bibtex/2082aceb6627344b5147092a28deadfac/situx}, day = 13, doi = {10.1007/s00607-019-00701-y}, interhash = {a8ec88a9cc20d571a0b832a8e7e2667b}, intrahash = {082aceb6627344b5147092a28deadfac}, issn = {1436-5057}, journal = {Computing}, keywords = {imported}, language = {english}, month = {February}, pages = {1--27}, publisher = {Springer International Publishing}, timestamp = {2019-09-17T13:27:01.000+0200}, title = {Interpretation and automatic integration of geospatial data into the Semantic Web}, url = {https://doi.org/10.1007/s00607-019-00701-y}, year = 2019 }